Abstract

Background

To quantify the variability among centers and to identify centers whose performance
are potentially outside of normal variability in the primary outcome and to propose
a guideline that they are outliers.

Methods

Novel statistical methodology using a Bayesian hierarchical model is used. Bayesian
methods for estimation and outlier detection are applied assuming an additive random
center effect on the log odds of response: centers are similar but different (exchangeable).
The Intraoperative Hypothermia for Aneurysm Surgery Trial (IHAST) is used as an example.
Analyses were adjusted for treatment, age, gender, aneurysm location, World Federation
of Neurological Surgeons scale, Fisher score and baseline NIH stroke scale scores.
Adjustments for differences in center characteristics were also examined. Graphical
and numerical summaries of the between-center standard deviation (sd) and variability,
as well as the identification of potential outliers are implemented.

Results

In the IHAST, the center-to-center variation in the log odds of favorable outcome
at each center is consistent with a normal distribution with posterior sd of 0.538 (95% credible interval: 0.397 to 0.726) after adjusting for the effects of important covariates. Outcome differences among
centers show no outlying centers. Four potential outlying centers were identified
but did not meet the proposed guideline for declaring them as outlying. Center characteristics
(number of subjects enrolled from the center, geographical location, learning over
time, nitrous oxide, and temporary clipping use) did not predict outcome, but subject
and disease characteristics did.

Conclusions

Bayesian hierarchical methods allow for determination of whether outcomes from a specific
center differ from others and whether specific clinical practices predict outcome,
even when some centers/subgroups have relatively small sample sizes. In the IHAST
no outlying centers were found. The estimated variability between centers was moderately
large.